Quantitative microbiome profiling links gut community variation to microbial load
Vandeputte et al.
The finding, in our words
Expressing taxa as cells per gram of stool showed faecal microbial load varies ~10-fold between healthy people and drives apparent compositional differences: relative-abundance data alone can misrepresent quantitative change.
A paraphrase to the Library’s standard, never the abstract. The source is one link away and is always the authority.
A multi-institution workshop made the case for a characterised whole-stool reference material so microbiome measurements can be standardised across labs, addressing the absence of defined faecal inputs.
A study of three children found that the region of stool sampled did not change microbial alpha diversity, while 22 of 176 metabolites varied; homogenising the stool mattered for metabolomics and short room-temperature storage had little effect, which supports simpler home collection protocols within the limits of so small a study.
This study found that stool samples self-collected on cards showed high correlation and agreement with ethanol-fixed samples for metagenomic sequencing, with negligible differences in microbial diversity. The results support the use of stool cards as a cost-effective alternative for decentralised sampling in epidemiologic studies, despite minor variations in individual species abundance.
This study compared OMNIgene Gut tubes and FTA cards for stool collection in a deployed setting, finding that OMNIgene yielded higher nucleic acid concentrations while both methods detected the majority of microbial genera. The authors conclude that distinct microbial abundance profiles between the two methods necessitate standardised protocols for field research.
This study compared two preservatives for stool samples and found that OMNIgene GUT OMR-200 produced less variation in metagenomic taxonomic data across different storage temperatures, supporting its use in decentralised field studies. The authors recommend absolute quantification to address bias in microbial measurements.